1. Field of the Invention
The present invention relates to a system and method for improving the performance of estimation techniques and for reducing the number of elements required for this estimation task when dealing with general purpose signals that are embedded in interference.
2. Discussion of the Related Art
Several signals processing techniques have been developed to process signals and estimate parameters of interest based on the knowledge of a reference signal. Efforts are generally made to reduce or suppress the interference received with the signal and this interference can be of various types such as noise, jamming and other users. When these signals possess a large bandwidth and require a large number of elements for estimation, the signal processing task becomes rather challenging.
Adaptive signal processing is particular technique designed to model, extract and track signals by weighting a set of discrete-time signals from a source, which can be an antenna or a general electronic equipment, in order to perform a desired task.
To compute the adaptive weights, these techniques typically combine several samples over a period of time. Generally, adaptive weights are calculated through the relationship Rw=p, where p is the steering vector with M coefficients, R is M×M the covariance matrix, and w is the weight vector with M elements. In order to identify the adaptive weights this relationship is simply manipulated to the following: w=R−1p. This equation requires a number of arithmetic operations that is proportional to M3, which is too complex for practical use.
In this context, existing adaptive signal processing techniques such as transversal linear filters with the least-mean square (LMS) algorithm are simple, have low complexity but usually have poor convergence performance. In contrast, adaptive filters with recursive least-squares (RLS) algorithms have fast convergence but require a significantly higher complexity than LMS recursions. Several attempts to provide cost-effective parameter estimators with fast convergence performance have been made in the last few decades through variable step size algorithms, data-reusing, averaging methods, sub-band and frequency-domain adaptive filters and RLS types algorithms with linear complexity such as lattice-based implementations, fast RLS algorithms, QR-decomposition-based RLS techniques and the more recent and promising reduced-rank adaptive filters.
The advantages of reduced-rank adaptive filters are their faster convergence speed and better tracking performance over existing techniques when dealing with large number of weights. Various reduced-rank methods and systems were based on principal components analysis, in which a computationally expensive singular value decomposition (SVD) to extract the signal subspace is required. Other recent techniques such as the multistage Wiener filter (MWF) of Goldstein et al. in “A multistage representation of the Wiener filter based on orthogonal projections”, IEEE Transactions of Information Theory, vol. 44, November, 1998—perform orthogonal decompositions in order to compute its parameters, leading to very good performance and a complexity inferior to those systems that require poor performance in systems with moderate to heavy loads.
In most applications, the process of calculating and altering the weights must be done in real-time. Because modern applications involve a large number of adaptive parameters and operate in non-stationary environments, the system requires a large amount of data to compute the estimates. However, in most practical situations the amount of data available is simply insufficient to provide accurate estimates. In addition, when a parameter estimator with a large number of weights is required to track a dynamic signal embedded in interference, it encounters difficulties in following the signal of interest and may fall or show unsatisfactory performance.
These and other deficiencies exist in current adaptive processing systems in the open literature and amongst the patented techniques so far. Therefore, a solution to these problems is needed providing a reduced rank adaptive processing system and method specifically designed to more accurately estimate and track signals that involve a large number of processing elements with low complexity and great flexibility.